Demand response strategy for microgrid energy management integrating electric vehicles, battery energy storage system, and distributed generators considering uncertainties

Abstract

The growing adoption of electric vehicles (EVs) in microgrids (MGs) necessitates effective energy scheduling while introducing several operational challenges for MG operators. The presented work integrates demand response (DR) programs into the operational framework of microgrids to address these challenges. The first phase of the proposed work estimates the optimal capacity of renewable distributed generators and the sizing and scheduling of battery energy storage systems (BESS) based on system load demand. For electric vehicle charging station (EVCS) modeling, $M_1/M_2/c$ queuing theory-based approach is utilized to estimate the need for minimum charging plugs to reduce waiting time for EV owners. This second stage introduces a mathematical model for the optimal energy scheduling of MG by implementing incentive and price-based DR schemes. The primary objective is to maximize the economic benefits for MG operators and potential DR participants. The two DR participants explored are EVCS and DR aggregators. The EVCS aggregators optimize charging schedules for EVs and charging/discharging schedules for BESS based on hourly electricity prices, while the DR aggregators encourage non-EV consumers to adjust their load demand according to hourly incentive rates. The uncertain behavior of RE sources, load demand, and electricity market price is analyzed using Hong’s $(2m+1)$ point estimation method. Furthermore, the energy management strategy optimally configures the MG with minimal power losses by imposing a network reconfiguration method. A day-ahead analysis of the proposed model leads to a 9.96% reduction in energy imported from the primary grid, resulting in an energy cost savings of 8.37%.